fix f16 path

src/common/transformations/src/transformations/convert_precision.cpp

@@ -607,7 +607,11 @@ bool fuse_type_to_parameter(const std::shared_ptr<ov::Node>& node,
             auto convert = std::make_shared<opset4::Convert>(param, to);
             for (auto& input : param_consumers) {
                 const auto consumer = input.get_node();
-                if (ov::is_type<ov::op::v0::Result>(consumer) || ov::is_type<ov::op::v0::Convert>(consumer)) {
+                if (ov::is_type<ov::op::v0::Result>(consumer) || ov::is_type<ov::op::v0::Convert>(consumer) ||
+                    // TODO: refactor after ngraph op defined
+                    // The fourth and fifth inputs are kvcache and should be directly connected to parameters
+                    (consumer->get_type_name() == std::string("PagedAttentionExtension") &&
+                     (input.get_index() == 3 || input.get_index() == 4))) {
                     continue;
                 }
                 input.replace_source_output(convert);

src/plugins/intel_cpu/src/nodes/kernels/scaled_attn/attn_memcpy.cpp

@@ -86,11 +86,11 @@ static void paged_attn_memcpy_kernel(const ov::intel_cpu::PlainTensor& k_input,
     parallel_for3d(B, H, L1, [&](size_t b, size_t h, size_t m) {
         auto block_idx = slot_mapping.ptr<int32_t>(b)[m];
         if (block_idx < 0) return;
-        attn_copy(past_k_output.ptr<T2>(block_idx, h, m, 0),
-                  k_input.ptr<T>(b, h, 0),
+        attn_copy(past_k_output.ptr<T2>(block_idx, h, 0),
+                  k_input.ptr<T>(b, h, m, 0),
                   S);
-        attn_copy(past_v_output.ptr<T2>(block_idx, h, m, 0),
-                  v_input.ptr<T>(b, h, 0),
+        attn_copy(past_v_output.ptr<T2>(block_idx, h, 0),
+                  v_input.ptr<T>(b, h, m, 0),
                   S);
     });
 }